The Need of an Optimal QoS Repository and Assessment Framework in Forming a Trusted Relationship in Cloud: A Systematic Review

Alghamdi, Ahmed; Hussain, Walayat; Alharthi, Abdulaziz; Almusheqah, Abdulla Badar

doi:10.1109/icebe.2017.55

Cited by 16 publications

(6 citation statements)

References 38 publications

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“…There has been a lot of work performed in the field of text extraction from videos. Karray et al [49] extracted text using classification methods. Hua et al [50] used edge detection, which is a type of text extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The disappearing gradient problem occurs in simple recurrent neural networks, but LSTM networks can learn meaningful information. They use different gates and memory cells to resolve the problem of vanishing gradient [49]. A bidirectional LSMT network is used in text recognition, as the learning pattern of text from right to left and left to right is very important [23].…”

Section: Proposed Cnn-rnn Attention Modelmentioning

confidence: 99%

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Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

et al. 2021

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According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Cnn-rnn Attention Modelmentioning

confidence: 99%

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

et al. 2021

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“…The QoS prediction is another extent that impact on consumer's decision. Authors 3,15–17 applied different prediction methods to analyse the prediction accuracy of cloud QoS data 18 . Authors 19 studied the use of web service modelling ontology (WSMO) to describe cloud services.…”

Section: Existing Cloud Service Discovery Approachesmentioning

confidence: 99%

“…It also assists a consumer in searching related cloud services in is the cloud marketplace. Such a repository could greatly assist consumers in locating cloud services 3 . Instead, consumers are currently using general search engines to find services.…”

Section: Introductionmentioning

confidence: 99%

Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services

Alkalbani

Hussain

2021

Int J Communication

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Summary The increase in the number of cloud services advertisements, needs for cloud services marketplace to enable significant interaction with cloud consumers. Majority of the existing literature has focused on developing algorithms (such as matching algorithms) and assumed the availability of cloud service information. Furthermore, little attention is given to the efficient discovery of cloud services over the internet. Existing approaches unable to describe a user‐friendly method of harvesting related cloud services from the web. Moreover, the existing literature lacks a comprehensive ontology to represent cloud services and a registry for cloud services publication and discovery. The incomplete information prevents discovering accurate services and deriving intelligence from cloud reviews data. The paper presents a framework for automatic derivation of cloud marketplace and cloud intelligence (ADCM&CI) that assist cloud consumers for an effective and efficient cloud service discovery. The framework depends on the capabilities of the Harvester as a Service (HaaS) crawler that provides a user‐friendly interface to extract real‐time cloud dataset. The paper used Protégé OWL a domain‐specific ontology to extract meaningful data from a semi‐structured repository and transform to SaaS ads attribute. The framework conducts sentimental analysis to excerpt the polarity of reviews that assist potential consumers in service selection. The paper considers three measures—precision, recall and F Score as a benchmark and evaluates the accuracy of the proposed approach using machine learning methods—SVM, KNN, Decision Tree and Naïve Bayes algorithms. Through experiments, we validate and demonstrate the suitability of the proposed framework for an effective and efficient cloud service discovery.

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“…However, for forming a long-term trusted relationship between the e-commerce service provider and a customer they should form a viable service level agreement (SLA) [17][18][19]. The compliance with SLA between both parties may be determined by gathering data for each HTTP/ HTTPS request and response send to the e-commerce service provider [20].…”

Section: A Cloud (Saas) and The E-commercementioning

confidence: 99%

SaaS E-Commerce Platforms Web Accessibility Evaluation

Sohaib

Naderpour

Hussain

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Web accessibility related to cloud computing is more concerned at the application level where a human interacts with an application via a user interface. Although previous research has identified web accessibility influences on website effectiveness, the evaluation of the relative importance of web accessibility on software-as-a-service (SaaS) e-commerce platform has not been empirically determined. This study evaluates the web accessibility of SaaS e-commerce platform websites. The web accessibility features from the cloud accessibility taxonomy framework were evaluated for people with disabilities such as sensory (hearing and vision), motor (limited use of hands) and cognitive (language and learning disabilities) impairments. We conducted an expert evaluation using Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The results show Shopify cloud-based e-commerce platform has a high number of web accessibility features from the proposed cloud accessibility framework followed by 3dCart, BigCommerce, Volusion, and WooCommerce.

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The Need of an Optimal QoS Repository and Assessment Framework in Forming a Trusted Relationship in Cloud: A Systematic Review

Cited by 16 publications

References 38 publications

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

Attention-Based CNN-RNN Arabic Text Recognition from Natural Scene Images

Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services

SaaS E-Commerce Platforms Web Accessibility Evaluation

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